Learning curve of RA-CUSUM analysis Multivariate logistic regression model showed that BMI, hypertension, and operation time were risk factors for surgical failure (P < 0.05, Table 5). We obtained the expected probability of surgical failure in each case according to the model predictions, thu...
This paper applies learning curve theory to implementation by developing a system dynamics model that includes two extensions to classic learning curve theory. First, the model includes a required output level for the individual. Second, the model includes a budget constraint on time that forces a ...
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. Moreover, many developed techniques only involve the process of feature extraction and classification [27]. So, accurate identification and detection of WBC is more risky. These reasons motivated this present research towards designing the optimized deep learning model. This article is structured as...
This paper applies learning curve theory to implementation by developing a system dynamics model that includes two extensions to classic learning curve theory. First, the model includes a required output level for the individual. Second, the model includes a budget constraint on time that forces a ...
Most existing methods use performance in downstream tasks, such as node classification and link prediction, to perform the model selection rather than establishing direct connections between the embedding dimension and the structural properties of the network. However, the performance for different tasks ...
To extract more information about model performance the confusion matrix is used. The confusion matrix helps us visualize whether the model is “confused” in discriminating between the two classes. As seen in the next figure, it is a 2×2 matrix. The labels of the two rows and columns are...
First, there is the effect of overlearning. Overlearning occurs when knowledge already acquired and understood perfectly is still being learned. The same applies to motor skills. In such cases, the forgetting curve does not appear. A proper and very valid example is driving a car. After ...
In radiomics, different feature normalization methods, such as z-Score or Min–Max, are currently utilized, but their specific impact on the model is unclear. We aimed to measure their effect on the predictive performance and the feature selection. We em
Lastly, we fine-tune \(U_\mathcal {S}\) in this new dataset, starting from the weights of the model trained on \(\mathcal {S}\), with a learning rate reduced by a factor of 100, for 10 extra epochs. During training, we monitor the AUC computed in the training set (including ...